Overview

Dataset statistics

Number of variables12
Number of observations124494
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory11.4 MiB
Average record size in memory96.0 B

Variable types

Categorical3
Numeric9

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
date has a high cardinality: 304 distinct values High cardinality
device has a high cardinality: 1169 distinct values High cardinality
attribute7 is highly correlated with attribute8High correlation
attribute8 is highly correlated with attribute7High correlation
attribute3 is highly correlated with attribute9High correlation
attribute7 is highly correlated with attribute8High correlation
attribute8 is highly correlated with attribute7High correlation
attribute9 is highly correlated with attribute3High correlation
attribute7 is highly correlated with attribute8High correlation
attribute8 is highly correlated with attribute7High correlation
attribute3 is highly correlated with attribute4 and 1 other fieldsHigh correlation
attribute4 is highly correlated with attribute3High correlation
attribute7 is highly correlated with attribute8High correlation
attribute8 is highly correlated with attribute7High correlation
attribute9 is highly correlated with attribute3High correlation
attribute2 is highly skewed (γ1 = 23.8579234) Skewed
attribute3 is highly skewed (γ1 = 82.712278) Skewed
attribute4 is highly skewed (γ1 = 41.50261118) Skewed
attribute7 is highly skewed (γ1 = 73.47645637) Skewed
attribute8 is highly skewed (γ1 = 73.47645637) Skewed
attribute9 is highly skewed (γ1 = 49.89927809) Skewed
attribute2 has 118110 (94.9%) zeros Zeros
attribute3 has 115359 (92.7%) zeros Zeros
attribute4 has 115156 (92.5%) zeros Zeros
attribute7 has 123036 (98.8%) zeros Zeros
attribute8 has 123036 (98.8%) zeros Zeros
attribute9 has 97358 (78.2%) zeros Zeros

Reproduction

Analysis started2021-10-22 15:15:26.930074
Analysis finished2021-10-22 15:15:43.474908
Duration16.54 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

date
Categorical

HIGH CARDINALITY

Distinct304
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size972.7 KiB
2015-01-02
 
1163
2015-01-01
 
1163
2015-01-03
 
1163
2015-01-04
 
1162
2015-01-05
 
1161
Other values (299)
118682 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015-01-01
2nd row2015-01-01
3rd row2015-01-01
4th row2015-01-01
5th row2015-01-01

Common Values

ValueCountFrequency (%)
2015-01-021163
 
0.9%
2015-01-011163
 
0.9%
2015-01-031163
 
0.9%
2015-01-041162
 
0.9%
2015-01-051161
 
0.9%
2015-01-061054
 
0.8%
2015-01-07798
 
0.6%
2015-01-09756
 
0.6%
2015-01-08756
 
0.6%
2015-01-12755
 
0.6%
Other values (294)114563
92.0%

Length

2021-10-22T11:15:43.562044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2015-01-021163
 
0.9%
2015-01-011163
 
0.9%
2015-01-031163
 
0.9%
2015-01-041162
 
0.9%
2015-01-051161
 
0.9%
2015-01-061054
 
0.8%
2015-01-07798
 
0.6%
2015-01-09756
 
0.6%
2015-01-08756
 
0.6%
2015-01-12755
 
0.6%
Other values (294)114563
92.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

device
Categorical

HIGH CARDINALITY

Distinct1169
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size972.7 KiB
Z1F0MA1S
 
304
S1F0GPXY
 
304
S1F0EGMT
 
304
W1F0JY02
 
304
S1F0FP0C
 
304
Other values (1164)
122974 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowS1F01085
2nd rowS1F0166B
3rd rowS1F01E6Y
4th rowS1F01JE0
5th rowS1F01R2B

Common Values

ValueCountFrequency (%)
Z1F0MA1S304
 
0.2%
S1F0GPXY304
 
0.2%
S1F0EGMT304
 
0.2%
W1F0JY02304
 
0.2%
S1F0FP0C304
 
0.2%
Z1F0KJDS304
 
0.2%
Z1F0QL3N304
 
0.2%
S1F0H6JG304
 
0.2%
Z1F0Q8RT304
 
0.2%
Z1F0QK05304
 
0.2%
Other values (1159)121454
97.6%

Length

2021-10-22T11:15:43.710085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
z1f0ma1s304
 
0.2%
s1f0ggpp304
 
0.2%
w1f0jxdl304
 
0.2%
w1f05x69304
 
0.2%
s1f0gced304
 
0.2%
w1f0fy92304
 
0.2%
z1f0kkn4304
 
0.2%
w1f0fzpa304
 
0.2%
s1f0e9ep304
 
0.2%
s1f0fgbq304
 
0.2%
Other values (1159)121454
97.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

failure
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.7 KiB
0
124388 
1
 
106

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0124388
99.9%
1106
 
0.1%

Length

2021-10-22T11:15:43.821109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-22T11:15:43.892137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0124388
99.9%
1106
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

attribute1
Real number (ℝ≥0)

Distinct123877
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122388103.2
Minimum0
Maximum244140480
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size972.7 KiB
2021-10-22T11:15:43.967154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12090104.4
Q161284762
median122797388
Q3183309640
95-th percentile231873846.4
Maximum244140480
Range244140480
Interquartile range (IQR)122024878

Descriptive statistics

Standard deviation70459334.22
Coefficient of variation (CV)0.5757041113
Kurtosis-1.199305658
Mean122388103.2
Median Absolute Deviation (MAD)61032236
Skewness-0.01114296352
Sum1.523658453 × 1013
Variance4.964517778 × 1015
MonotonicityNot monotonic
2021-10-22T11:15:44.102188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8919655226
 
< 0.1%
16504891226
 
< 0.1%
5719236026
 
< 0.1%
16949024823
 
< 0.1%
5718013615
 
< 0.1%
16946734415
 
< 0.1%
1219497615
 
< 0.1%
16504062415
 
< 0.1%
8916264815
 
< 0.1%
16504514413
 
< 0.1%
Other values (123867)124305
99.8%
ValueCountFrequency (%)
011
< 0.1%
20481
 
< 0.1%
20562
 
< 0.1%
21681
 
< 0.1%
37841
 
< 0.1%
42241
 
< 0.1%
44801
 
< 0.1%
45601
 
< 0.1%
82801
 
< 0.1%
86161
 
< 0.1%
ValueCountFrequency (%)
2441404801
< 0.1%
2441386001
< 0.1%
2441365521
< 0.1%
2441356881
< 0.1%
2441332401
< 0.1%
2441329361
< 0.1%
2441327521
< 0.1%
2441317121
< 0.1%
2441294161
< 0.1%
2441278401
< 0.1%

attribute2
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct558
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159.4847623
Minimum0
Maximum64968
Zeros118110
Zeros (%)94.9%
Negative0
Negative (%)0.0%
Memory size972.7 KiB
2021-10-22T11:15:44.392253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8
Maximum64968
Range64968
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2179.65773
Coefficient of variation (CV)13.66687136
Kurtosis626.8205749
Mean159.4847623
Median Absolute Deviation (MAD)0
Skewness23.8579234
Sum19854896
Variance4750907.822
MonotonicityNot monotonic
2021-10-22T11:15:44.507265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0118110
94.9%
2344281
 
0.2%
8260
 
0.2%
24254
 
0.2%
40201
 
0.2%
4960175
 
0.1%
424169
 
0.1%
16166
 
0.1%
88152
 
0.1%
552140
 
0.1%
Other values (548)4586
 
3.7%
ValueCountFrequency (%)
0118110
94.9%
8260
 
0.2%
16166
 
0.1%
24254
 
0.2%
32132
 
0.1%
40201
 
0.2%
4890
 
0.1%
56104
 
0.1%
6426
 
< 0.1%
7235
 
< 0.1%
ValueCountFrequency (%)
649681
 
< 0.1%
647927
< 0.1%
6478411
< 0.1%
647768
< 0.1%
6473613
< 0.1%
6472813
< 0.1%
6458417
< 0.1%
644721
 
< 0.1%
644641
 
< 0.1%
622961
 
< 0.1%

attribute3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.940454962
Minimum0
Maximum24929
Zeros115359
Zeros (%)92.7%
Negative0
Negative (%)0.0%
Memory size972.7 KiB
2021-10-22T11:15:44.625305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum24929
Range24929
Interquartile range (IQR)0

Descriptive statistics

Standard deviation185.7473207
Coefficient of variation (CV)18.68599791
Kurtosis10473.5883
Mean9.940454962
Median Absolute Deviation (MAD)0
Skewness82.712278
Sum1237527
Variance34502.06713
MonotonicityNot monotonic
2021-10-22T11:15:44.741331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0115359
92.7%
13274
 
2.6%
2749
 
0.6%
7298
 
0.2%
34293
 
0.2%
5278
 
0.2%
21269
 
0.2%
4268
 
0.2%
9262
 
0.2%
8251
 
0.2%
Other values (37)3193
 
2.6%
ValueCountFrequency (%)
0115359
92.7%
13274
 
2.6%
2749
 
0.6%
3113
 
0.1%
4268
 
0.2%
5278
 
0.2%
7298
 
0.2%
8251
 
0.2%
9262
 
0.2%
10241
 
0.2%
ValueCountFrequency (%)
249294
 
< 0.1%
2693179
0.1%
21126
 
< 0.1%
1331240
0.2%
13265
 
< 0.1%
11621
 
< 0.1%
40684
 
0.1%
3825
 
< 0.1%
3776
 
< 0.1%
3236
 
< 0.1%

attribute4
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct115
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.741120054
Minimum0
Maximum1666
Zeros115156
Zeros (%)92.5%
Negative0
Negative (%)0.0%
Memory size972.7 KiB
2021-10-22T11:15:44.856363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum1666
Range1666
Interquartile range (IQR)0

Descriptive statistics

Standard deviation22.90850654
Coefficient of variation (CV)13.15733886
Kurtosis2467.96284
Mean1.741120054
Median Absolute Deviation (MAD)0
Skewness41.50261118
Sum216759
Variance524.7996719
MonotonicityNot monotonic
2021-10-22T11:15:44.970371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0115156
92.5%
63681
 
3.0%
1889
 
0.7%
2711
 
0.6%
3466
 
0.4%
12454
 
0.4%
4359
 
0.3%
10294
 
0.2%
112245
 
0.2%
5231
 
0.2%
Other values (105)2008
 
1.6%
ValueCountFrequency (%)
0115156
92.5%
1889
 
0.7%
2711
 
0.6%
3466
 
0.4%
4359
 
0.3%
5231
 
0.2%
63681
 
3.0%
7175
 
0.1%
8170
 
0.1%
945
 
< 0.1%
ValueCountFrequency (%)
16669
< 0.1%
10746
 
< 0.1%
10333
 
< 0.1%
8411
 
< 0.1%
7631
 
< 0.1%
5331
 
< 0.1%
5294
 
< 0.1%
5216
 
< 0.1%
48718
< 0.1%
48615
< 0.1%

attribute5
Real number (ℝ≥0)

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.22266937
Minimum1
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size972.7 KiB
2021-10-22T11:15:45.094415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q18
median10
Q312
95-th percentile58
Maximum98
Range97
Interquartile range (IQR)4

Descriptive statistics

Standard deviation15.943028
Coefficient of variation (CV)1.120958913
Kurtosis12.15213494
Mean14.22266937
Median Absolute Deviation (MAD)2
Skewness3.483679387
Sum1770637
Variance254.1801417
MonotonicityNot monotonic
2021-10-22T11:15:45.214968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
822145
17.8%
913597
10.9%
1112792
10.3%
1011480
9.2%
711271
9.1%
129843
7.9%
68542
 
6.9%
136006
 
4.8%
143517
 
2.8%
53429
 
2.8%
Other values (50)21872
17.6%
ValueCountFrequency (%)
1173
 
0.1%
2203
 
0.2%
3815
 
0.7%
4933
 
0.7%
53429
 
2.8%
68542
 
6.9%
711271
9.1%
822145
17.8%
913597
10.9%
1011480
9.2%
ValueCountFrequency (%)
98224
 
0.2%
95672
0.5%
94224
 
0.2%
92448
0.4%
91215
 
0.2%
90357
0.3%
89224
 
0.2%
78224
 
0.2%
70224
 
0.2%
68448
0.4%

attribute6
Real number (ℝ≥0)

Distinct44838
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean260172.6577
Minimum8
Maximum689161
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size972.7 KiB
2021-10-22T11:15:45.334989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile46
Q1221452
median249799.5
Q3310266
95-th percentile443047.8
Maximum689161
Range689153
Interquartile range (IQR)88814

Descriptive statistics

Standard deviation99151.07855
Coefficient of variation (CV)0.3810972276
Kurtosis1.907777201
Mean260172.6577
Median Absolute Deviation (MAD)35382.5
Skewness-0.3752846096
Sum3.238993485 × 1010
Variance9830936377
MonotonicityNot monotonic
2021-10-22T11:15:45.454021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31777
 
0.6%
44708
 
0.6%
27636
 
0.5%
26520
 
0.4%
29441
 
0.4%
36337
 
0.3%
35290
 
0.2%
52282
 
0.2%
45246
 
0.2%
28216
 
0.2%
Other values (44828)120041
96.4%
ValueCountFrequency (%)
819
 
< 0.1%
9172
0.1%
1251
 
< 0.1%
1836
 
< 0.1%
1930
 
< 0.1%
206
 
< 0.1%
2158
 
< 0.1%
2371
 
0.1%
24123
0.1%
25184
0.1%
ValueCountFrequency (%)
6891611
< 0.1%
6890621
< 0.1%
6890351
< 0.1%
6889641
< 0.1%
6889522
< 0.1%
6879011
< 0.1%
6878021
< 0.1%
6877751
< 0.1%
6877061
< 0.1%
6876942
< 0.1%

attribute7
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.292528154
Minimum0
Maximum832
Zeros123036
Zeros (%)98.8%
Negative0
Negative (%)0.0%
Memory size972.7 KiB
2021-10-22T11:15:45.577037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum832
Range832
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.436923979
Coefficient of variation (CV)25.42293409
Kurtosis6876.273007
Mean0.292528154
Median Absolute Deviation (MAD)0
Skewness73.47645637
Sum36418
Variance55.30783827
MonotonicityNot monotonic
2021-10-22T11:15:45.669064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0123036
98.8%
8793
 
0.6%
16397
 
0.3%
2465
 
0.1%
4836
 
< 0.1%
3235
 
< 0.1%
12823
 
< 0.1%
17620
 
< 0.1%
4020
 
< 0.1%
613
 
< 0.1%
Other values (18)56
 
< 0.1%
ValueCountFrequency (%)
0123036
98.8%
613
 
< 0.1%
8793
 
0.6%
16397
 
0.3%
222
 
< 0.1%
2465
 
0.1%
3235
 
< 0.1%
4020
 
< 0.1%
4836
 
< 0.1%
566
 
< 0.1%
ValueCountFrequency (%)
8322
 
< 0.1%
7441
 
< 0.1%
7364
 
< 0.1%
4961
 
< 0.1%
4241
 
< 0.1%
3125
 
< 0.1%
2722
 
< 0.1%
2401
 
< 0.1%
2161
 
< 0.1%
17620
< 0.1%

attribute8
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.292528154
Minimum0
Maximum832
Zeros123036
Zeros (%)98.8%
Negative0
Negative (%)0.0%
Memory size972.7 KiB
2021-10-22T11:15:45.769092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum832
Range832
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.436923979
Coefficient of variation (CV)25.42293409
Kurtosis6876.273007
Mean0.292528154
Median Absolute Deviation (MAD)0
Skewness73.47645637
Sum36418
Variance55.30783827
MonotonicityNot monotonic
2021-10-22T11:15:45.861107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0123036
98.8%
8793
 
0.6%
16397
 
0.3%
2465
 
0.1%
4836
 
< 0.1%
3235
 
< 0.1%
12823
 
< 0.1%
17620
 
< 0.1%
4020
 
< 0.1%
613
 
< 0.1%
Other values (18)56
 
< 0.1%
ValueCountFrequency (%)
0123036
98.8%
613
 
< 0.1%
8793
 
0.6%
16397
 
0.3%
222
 
< 0.1%
2465
 
0.1%
3235
 
< 0.1%
4020
 
< 0.1%
4836
 
< 0.1%
566
 
< 0.1%
ValueCountFrequency (%)
8322
 
< 0.1%
7441
 
< 0.1%
7364
 
< 0.1%
4961
 
< 0.1%
4241
 
< 0.1%
3125
 
< 0.1%
2722
 
< 0.1%
2401
 
< 0.1%
2161
 
< 0.1%
17620
< 0.1%

attribute9
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct65
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.45152377
Minimum0
Maximum18701
Zeros97358
Zeros (%)78.2%
Negative0
Negative (%)0.0%
Memory size972.7 KiB
2021-10-22T11:15:45.968132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11
Maximum18701
Range18701
Interquartile range (IQR)0

Descriptive statistics

Standard deviation191.425623
Coefficient of variation (CV)15.37367045
Kurtosis4050.190542
Mean12.45152377
Median Absolute Deviation (MAD)0
Skewness49.89927809
Sum1550140
Variance36643.76914
MonotonicityNot monotonic
2021-10-22T11:15:46.080163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
097358
78.2%
19436
 
7.6%
23722
 
3.0%
32327
 
1.9%
41396
 
1.1%
6797
 
0.6%
7774
 
0.6%
5735
 
0.6%
8733
 
0.6%
10641
 
0.5%
Other values (55)6575
 
5.3%
ValueCountFrequency (%)
097358
78.2%
19436
 
7.6%
23722
 
3.0%
32327
 
1.9%
41396
 
1.1%
5735
 
0.6%
6797
 
0.6%
7774
 
0.6%
8733
 
0.6%
9335
 
0.3%
ValueCountFrequency (%)
187015
 
< 0.1%
101374
 
< 0.1%
72265
 
< 0.1%
27946
 
< 0.1%
263784
0.1%
2522179
0.1%
22705
 
< 0.1%
22691
 
< 0.1%
18645
 
< 0.1%
1165118
0.1%

Interactions

2021-10-22T11:15:41.476958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:29.974990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:31.473649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:33.154817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:34.556990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:36.138958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:37.500855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:39.026788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:40.255685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:41.628006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:30.172280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:31.625668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:33.298850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:34.816074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:36.295997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:37.655911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:39.161807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:40.389727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:41.776039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:30.345332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:31.778711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:33.441889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:34.998114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:36.453041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:37.818944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:39.300833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:40.529759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:41.920059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:30.509356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:31.919748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:33.584915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:35.178682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:36.602755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:37.972502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:39.433974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:40.662788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:42.070092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:30.681902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:32.070808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:33.726960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:35.339727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:36.754033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:38.130076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:39.571187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:40.798819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:42.217574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:30.837950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:32.217837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:33.936994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:35.482751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:36.900726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:38.282604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:39.705003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:40.933837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:42.375021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:31.009976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:32.371924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:34.113903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:35.664846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:37.058768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:38.570680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:39.848091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:41.077882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:42.525071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:31.169024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:32.845761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:34.257930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:35.814886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:37.203795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:38.717712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:39.979078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:41.203910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:42.665101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:31.320612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:32.996781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:34.397969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:35.969931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:37.343826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:38.867752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:40.111138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-22T11:15:41.332933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-10-22T11:15:46.202185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-22T11:15:46.375847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-22T11:15:46.551083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-22T11:15:46.726568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-22T11:15:42.875139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-22T11:15:43.178842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

datedevicefailureattribute1attribute2attribute3attribute4attribute5attribute6attribute7attribute8attribute9
02015-01-01S1F010850215630672560526407438007
12015-01-01S1F0166B0613706800306403174000
22015-01-01S1F01E6Y017329596800012237394000
32015-01-01S1F01JE00796940240006410186000
42015-01-01S1F01R2B013597048000015313173003
52015-01-01S1F01TD506883748800416413535001
62015-01-01S1F01XDJ02277216320008402525000
72015-01-01S1F023H201415036000011949446216163
82015-01-01S1F02A0J0821784001014311869000
92015-01-01S1F02DZ2011644009603239940790500164

Last rows

datedevicefailureattribute1attribute2attribute3attribute4attribute5attribute6attribute7attribute8attribute9
1244842015-11-02W1F0SJJ204752532000012357421000
1244852015-11-02Z1F0GB8A0928231920009357127000
1244862015-11-02Z1F0GE1M022287870400010349826000
1244872015-11-02Z1F0KJDS07988364800011358121000
1244882015-11-02Z1F0KKN402187657120009353525000
1244892015-11-02Z1F0MA1S01831022400010353705880
1244902015-11-02Z1F0Q8RT0172556680961074113327920013
1244912015-11-02Z1F0QK0501902912048320011350410000
1244922015-11-02Z1F0QL3N022695340800012358980000
1244932015-11-02Z1F0QLC101757284000010351431000

Duplicate rows

Most frequently occurring

datedevicefailureattribute1attribute2attribute3attribute4attribute5attribute6attribute7attribute8attribute9# duplicates
02015-07-10S1F0R4Q8019272139200082137000002